Human Disease


Human Disease


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Recent technical development has made it possible to envisage an era of great scientific discoveries in gene level. Identifying disease-contributory variants for various human genetic diseases will greatly improve diagnosis and boost the development of therapies.

However, integrated tools to extract meaningful data from the abundantly accessible DNA sequencing data are still in great demand. Several tools have been developed to build up the pipeline from aligning the raw sequencing reads to a reference genome (BWA, Bowtie), performing data quality control and calling single-nucleotide variants (GATK) or structural variants (CNVnator and ERDS). But researchers still have a long way to go to fetch mutations associated with the exact human disease. To help researchers understand the functional content within the data and perform prioritization analysis even on your portable devices, we developed an online tool. After putting a VCF file in, it can carry out quality control (remove false positive SNVs) and rank the high quality variants automatically, generating an analyzing report with ranked high quality variants. We name it SNVfilter.

SNVfilter is a tool for removing sequencing errors and estimating the pathogenicity of variants generated from WES data which could: 1) filter out variants generated by sequencing or systematical error of the variants using linear regression model; 2) estimate the pathogenicity of variants based on a model constructed upon Support Vector Machine (SVM); 3)provide additional supporting information including: i) frequency of these variants and effect of these variants on genes (codon and residue Change); ii) whether genetic phenotypes of these genes were reported in human; iii) phenotype of these genes in mouse model; iv)tissue specific mRNA expression of these genes; v) functional enrichment analysis for these genes (including enriched GO, pathway and protein domains); vi) protein-protein interactional network associated with the phenotype.

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